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AI evidence extraction

Disclosing generative AI use for writing assistance should be voluntary

Mohammad Hosseini, Bert Gordijn, Gregory E. Kaebnick, Kristi Holmes · Research Ethics · 2025

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

10/10
Relevance
1/4
Quality (LMQS)
I
Evidence
12
Citations
5.02
FWCI
Top 10%
Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1177/17470161251345499

Methodology & findings

Study design

Argumentative position paper presenting a reasoned critique and reversal of the authors' previous stance on GenAI disclosure policies, based on conceptual analysis rather than empirical research.

Main result

The authors argue that "the case for mandatory disclosure of GenAI for writing assistance continues to diverge from the initial justifications for disclosure" and present three main reasons: "(1) the credit due to machines for assisting researchers is moving below the threshold of requiring recognition; (2) it is impractical (if not impossible) to accurately specify what parts of the text are human-/GenAI-generated; and (3) disclosures could increase biases against non-native speakers of the English language and compromise the integrity of the peer review system."

Reports effect sizes.

Research paradigm

Interpretive/argumentative (philosophical position paper)

Author conclusions

The authors conclude that "it should be up to the authors of manuscripts to disclose their use of GenAI for writing assistance." They further recommend that "in disciplines where writing is the hallmark of originality, or when authors believe disclosure is beneficial, a voluntary checkbox in manuscript submission systems, visible only after publication (rather than a free-text note in the manuscripts) would be preferable."

Risk of bias

Author position change: the authors acknowledge reversing their previous stance, which may introduce confirmation bias toward supporting voluntary disclosure; Lack of empirical evidence: arguments are conceptual rather than data-driven; Selection of justifications: the paper critiques three specific original justifications without systematic review of all arguments in the disclosure debate; No stakeholder input: no evidence presented from researchers, editors, or peer reviewers regarding practical impacts of disclosure policies

Open questions raised

  • The paper does not explicitly identify future research directions or gaps, but implicitly suggests gaps in: (1) empirical quantification of machine contribution thresholds in academic writing, (2) technical methods for accurately detecting and specifying human vs. AI-generated text, and (3) empirical assessment of how GenAI disclosure policies affect bias against non-native English speakers and peer review integrity.
Data: not_statedCode: not_statedExtracted from: pdfAgreement 76%

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